解释def adaPoint(box, pro): box_pro = box if pro != 1.0: box_pro = box / pro box_pro = np.trunc(box_pro) return box_pro # 四边形顶点排序,[top-left, top-right, bottom-right, bottom-left] def orderPoints(pts): rect = np.zeros((4, 2), dtype="float32") s = pts.sum(axis=1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] diff = np.diff(pts, axis=1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] return rect # 计算长宽 def pointDistance(a, b): return int(np.sqrt(np.sum(np.square(a - b)))) # 透视变换 def warpImage(image, box): w, h = pointDistance(box[0], box[1]), pointDistance(box[1], box[2]) dst_rect = np.array([[0, 0], [w - 1, 0], [w - 1, h - 1], [0, h - 1]], dtype='float32') M = cv2.getPerspectiveTransform(box, dst_rect) warped = cv2.warpPerspective(image, M, (w, h)) return warped
时间: 2023-11-28 18:06:41 浏览: 43
这是一个用于透视变换的函数,主要包括以下几个步骤:
1. adaPoint函数:根据给定的比例pro,将四边形的顶点坐标进行适当的缩放,并将坐标转换为整数类型;
2. orderPoints函数:对四边形的顶点进行排序,使其按照[top-left, top-right, bottom-right, bottom-left]的顺序排列;
3. pointDistance函数:计算两个点之间的欧氏距离;
4. warpImage函数:通过透视变换将原始图像image转换为目标四边形box所确定的矩形区域内的图像。具体实现方法是:
(a) 计算目标矩形的宽w和高h,以及目标矩形的四个顶点坐标dst_rect;
(b) 使用cv2.getPerspectiveTransform函数计算从原始四边形box到目标矩形dst_rect的透视变换矩阵M;
(c) 使用cv2.warpPerspective函数对原始图像image进行透视变换,得到目标矩形内的图像warped。
透视变换常用于图像校正、文档扫描等领域。
相关问题
total_obj_num_distance_dict = {10: 0, 20: 0, 50: 0, 100: 0, 'nocare': 0} tolerance_num_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} tolerance_num_vanish_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} tolerance_num_via_box = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} tolerance_num_combine = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} distance_error_sum_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} distance_error_sum_vanish_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} distance_error_sum_via_box = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} distance_error_sum_combine = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0}整理代码
可以使用字典的 `items()` 方法来遍历字典的键值对,然后按照需要的格式整理代码。以下是一个示例代码:
```python
def format_dict_code(dictionary):
code = ""
for key, value in dictionary.items():
code += f"{key}: {value}, "
code = "{" + code.rstrip(", ") + "}"
return code
total_obj_num_distance_dict_code = format_dict_code(total_obj_num_distance_dict)
tolerance_num_point_code = format_dict_code(tolerance_num_point)
tolerance_num_vanish_point_code = format_dict_code(tolerance_num_vanish_point)
tolerance_num_via_box_code = format_dict_code(tolerance_num_via_box)
tolerance_num_combine_code = format_dict_code(tolerance_num_combine)
distance_error_sum_point_code = format_dict_code(distance_error_sum_point)
distance_error_sum_vanish_point_code = format_dict_code(distance_error_sum_vanish_point)
distance_error_sum_via_box_code = format_dict_code(distance_error_sum_via_box)
distance_error_sum_combine_code = format_dict_code(distance_error_sum_combine)
print(f"total_obj_num_distance_dict = {total_obj_num_distance_dict_code}")
print(f"tolerance_num_point = {tolerance_num_point_code}")
print(f"tolerance_num_vanish_point = {tolerance_num_vanish_point_code}")
print(f"tolerance_num_via_box = {tolerance_num_via_box_code}")
print(f"tolerance_num_combine = {tolerance_num_combine_code}")
print(f"distance_error_sum_point = {distance_error_sum_point_code}")
print(f"distance_error_sum_vanish_point = {distance_error_sum_vanish_point_code}")
print(f"distance_error_sum_via_box = {distance_error_sum_via_box_code}")
print(f"distance_error_sum_combine = {distance_error_sum_combine_code}")
```
这段代码将会输出整理好的代码,你可以根据需要将其复制到你的程序中。
def _get_iou_types(model): model_without_ddp = model if isinstance(model, torch.nn.parallel.DistributedDataParallel): model_without_ddp = model.module iou_types = ["bbox"] return iou_types
这段代码定义了一个名为`_get_iou_types()`的函数,用于获取模型的IoU类型。
函数接受一个模型对象`model`作为参数,并返回一个IoU类型的列表。
首先,函数将输入的模型对象赋值给`model_without_ddp`变量。如果`model`是`torch.nn.parallel.DistributedDataParallel`类型的对象,则通过`model.module`获取原始模型对象,即去除了分布式数据并行封装的模型对象。
然后,函数创建一个包含一个元素的列表`iou_types`,其中元素为字符串`"bbox"`。这表示该函数目前仅支持边界框(bounding box)类型的IoU计算。
最后,函数返回`iou_types`列表,即IoU类型的列表。
以下是一个示例:
```python
def _get_iou_types(model):
model_without_ddp = model
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
model_without_ddp = model.module
iou_types = ["bbox"]
return iou_types
# 使用示例
model = torchvision.models.resnet50()
iou_types = _get_iou_types(model)
print(iou_types)
```
在上述示例中,我们传入了一个ResNet-50模型对象`model`给`_get_iou_types()`函数,然后打印输出IoU类型的列表。
如果还有其他问题,请随时提问。